In just 10 years, speech analytics has become an essential transformational tool for contact centers, and is well on its way to being considered mission-critical. Adoption of speech analytics solutions has been rapid for two primary reasons. The obvious one is that it is the only application that structures customer conversations and converts them into metadata that can be analyzed. The second reason is that these solutions are sold by vendors that appreciate the importance of making their findings useful and actionable. If the vendors had not been practical and responsive to the needs of their customers, speech analytics sales would be lagging.

Speech analytics was the first true analytics application to enter contact centers. (Some may argue that workforce management led the way, but this is a different type of solution.) This presented vendors and users with significant challenges. Contact center managers were not accustomed to hiring highly experienced business analysts with the skill sets required to optimize the use of speech analytics. Simultaneously, the vendors were struggling to build user interfaces (UIs) that simplified the presentation of data and findings while empowering users to perform necessary and often sophisticated searches and analysis. In the end, the groups met in the middle: Leading users of speech analytics are employing business analysts to oversee speech analytics initiatives while, at the same time, vendors have delivered UIs that hide many steps and some of the complexity involved in using speech analytics. The latter is a trend that is picking up momentum; vendors are delivering more packaged applications that come with relevant libraries, searches, queries, and reports. Yet there is still opportunity for significant improvements in how information is delivered to end users.

The numbers tell the story

Speech analytics has been a great success in contact centers, as the numbers indicate. The compound annual growth rate of seats between 2006 and 2012 (including the first seven months of 2013) was 44.6 percent. In 2006, there were 176,825 seats of speech analytics in the market. By the end of 2012, there were 2,332,733 seats. DMG estimates that the revenue size of the contact center speech analytics market was $214 million on a worldwide basis by the end of calendar year 2012. (This includes software, maintenance, professional services, etc.)

The primary disappointment with speech analytics is that the applications have not found wider acceptance in the greater enterprise. Vendors have not been effective in selling speech analytics outside the contact center, despite the great value and benefits these solutions yield. This remains a major opportunity for the market, but it's one that will take a long time to address, as it will require salespeople to speak "enterprise" and have contacts and relationships in departments other than contact centers.

Speech analytics and the customer journey

The buzzphrase of the moment in contact centers is customer journey. Enterprises are striving to measure and evaluate what happens with customers at every touch point, from the initial touch via a business Web site (in many cases) through the close of the deal by a salesperson. Speech analytics is essential for gleaning customer insights from contact center interactions. These solutions are used to understand why people call and whether they were satisfied or dissatisfied with the quality of the experience. When this data is combined with Web analytics, interactive voice response system analytics, text analytics, and desktop analytics (which grabs data from the CRM application), enterprises are able to evaluate a substantial portion of the customer journey. (Data about the live sales aspect is available only from the CRM system, and is therefore still limited.) This data can be used to identify and address areas of opportunity, but only if an organization has a system that can bring it all together, analyze it, and produce actionable results. This is where big data fits in.

Analyzing big data

Contact center infrastructure and, in particular, the automatic call distributor generate a massive amount of data. Big data is not new for contact centers; the opportunity is to use it to improve the performance of the contact center and other enterprise systems and